Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification

• URL: http://arxiv.org/abs/2305.12703v1
• Date: Mon, 22 May 2023 04:26:18 GMT
• Title: Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification
• Authors: Zhuo Li, Jingze Lu, Zhenduo Zhao, Wenchao Wang, Pengyuan Zhang
• Abstract summary: We propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment. To prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm.
• Score: 17.284276598514502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment (PGMVG clustering). To fully exploit the relationships among utterances and the complementarity among multiple models, our method constructs multiple k-nearest neighbors graphs based on diverse models and generates high-confidence edges using a voting mechanism. Further, to maximize the intra-class diversity, the connected subgraph is utilized to obtain the initial pseudo-labels. Finally, to prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm to decide whether merging sub-classes.

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